GeneaBloggers

Wednesday, 16 November 2016

FAN Principles Unfolded



What is the FAN Principle, and when would we use it? What generalisations of it can be employed? How does it relate to analytical methods from outside of genealogy? How do the different methods relate to our intended goals?

There is some confusion over what the FAN acronym stands for. I previously thought that it was Family, Associates, and Neighbours;[1] and later became Friends, Associates, and Neighbours,[2] possibly because it’s so easy to lapse into “Friends, Romans, and countrymen …”.[3]. The historical difference between friends (or enemies) and weaker associates could be a subjective one, and so not as useful to us in directing our research; however, knowing the extent or a person’s family might also be part of the goal to which this technique is applied in the first place. More recently, the terms get merged as Friends & Family, Associates, and Neighbours, with the acronym being unofficially extended to FFAN.

The sources I gave for the two variants of FAN, above, were from the same year and the same author (Elizabeth Shown Mills) and so didn’t support my initial impression; I had to get more details. According to Elizabeth, she started using the term FAN Principle in her Advanced Research Methodology (ARM) track at the Institute of Genealogy and Historical Research (IGHR), Samford University, during the early 1990s. That ARM track commenced in 1986, but its inspiration came the previous year when a defeatist response in the APG quarterly newsletter prompted her to analyse her own successes and failures. At that time, she emphasised neighbours and associates since she didn’t believe that family needed emphasising. Also, whole-family genealogy, as opposed to direct-line genealogy, was comparatively uncommon, and what there was mostly amounted to just following males with the same surname. Her technique was much wider than this, and required individuals to be placed in their respective community context in order to find new sources of relevant information. After several years of teaching this, she hit upon the notion that “Every ancestor had their FAN Club: Friends, Associates, and Neighbours.” With the explosion of Internet genealogy during the mid-2000s, whole-family research (not the same as simple name gathering) had become positively rare, and that’s when she started referring to Friends & Family, Associates, and Neighbours.

The FAN Principle is a research method for studying individuals in the context of their FAN Club in order to widen the search for relevant information.[4]  It is employed when we have no documents that give us direct evidence about a person’s identity, origin, and/or parentage. Although commercial genealogy may suggest that you can “build your tree” directly from their records, we all know that this rarely works; it’s not long before we can’t find someone, or we can’t identify someone (usually a woman), or there are alternatives that are too close for us to distinguish. You’re then in the world of inferential genealogy where you have to study the scant information available and make an argument for what the truth might have been — the better the argument, the more reliable the conclusion.

FAN Example

There’s a concise example of the FAN Principle provided in QuickLesson 11. It describes a Mary Smith who married a James Boyd in 1853, and shows how correlating various sources relating to her FAN Club allowed her family to be identified.

In this case, because there were no sources directly identifying Mary, it begins by looking at the most obvious person in her FAN Club: her husband, and then looking at his FAN Club. This is an accepted technique for identifying women during those times as they would be conspicuous by their absence in most records.

Targeted research to identify Mary Smith via her husband
Figure 1 – Targeted research to identify Mary Smith via her husband.

The above diagram illustrates that different sources relate to different sets of associates of the target person: James Boyd. For simplicity, it doesn’t show all the associates in this example, or all the sources; the following table includes all the sources.


Marriage
Road order
Land registry
1850 census
Boyd family
John


James
Mary


Andrew


Franklin

Smith neighbours
William



Jane



Samuel

William

Joseph

Thomas



Mary



Table 1 – Relationship of associates and sources for Boyd/Smith example.

By correlating the information provided by those sources, and by considering their contexts (dates, ages, occupation, etc.), then an argument is made for the wife of James Boyd being Mary C. Smith, daughter of the neighbouring William and Jane Smith.

Cluster Analysis

The FAN principle is also described by the terms Cluster Research[5] or Cluster Genealogy[6], but not the term Cluster Analysis. Cluster Analysis is a long-standing research method that has been applied to many different fields. I will take a brief tour of it in order to determine what, if any, relationship exists to the FAN Principle.

Cluster analysis separates data (or objects) into groups that are meaningful, useful, or both. Methods of cluster analysis fall into two broad types: ones where the clustering is evident in the data itself (empirical) and ones where we deliberately categorise data according to some shared property (categorical).

With empirical cluster analysis, data is typically plotted in some data space (e.g. pressure against temperature) or geographical space and the distribution of points examined for clusters. Although it’s usually easy to distinguish clusters visually, there are many different algorithms for locating them and establishing their boundaries. It would then be necessary to explain the number or shape of the clusters, or even their very existence.

Empirical cluster analysis: clusters evident in the data, and require explanation
Figure 2 – Empirical cluster analysis: clusters evident in the data, and require explanation.

One example of this method might be when analysing the geographical distribution of some disease or ailment. A genealogical example might be when looking at the distribution and movements of a family.

With categorical cluster analysis, data (or objects) are separated into groups that reflect some common attribute or property. This may be an abstraction to support statistics or summarisation, or a precursor to some other type of analysis. It is this type, therefore, to which the FAN Principle is related; the groups of family, friends, neighbours, and other associates, are the clusters into which we have separated the general associates of a target person.

Categorical cluster analysis: separation in preparation for some other study
Figure 3 – Categorical cluster analysis: separation in preparation for some other study.

Because the conceptual clusters have been predefined in this method then we might expect to see alternative results if we change them. In fact, in both types of analysis, clusters may be either strict partitional (each object only in one cluster), hierarchical (object in a child cluster is also in the containing parent cluster), or overlapping (object may be in multiple, non-exclusive clusters).

Overlapping, non-exclusive clusters
Figure 4 – Overlapping, non-exclusive clusters.

The FAN Club clusters are overlapping rather than hierarchical. For instance, not all family are neighbours, and not all neighbours are family.

There exists a specific cluster variant that deserves a mention: graph-based. In this variant, objects in a cluster are connected to other members of the same cluster by some relationship type, and not connected to members of other clusters. This is different to the clusters of objects that share a common property. At a stretch, it might be possible to describe a family cluster in this way as all its members are connected by some sort of family relationship; however, it is not particularly relevant to the FAN Club as associations there are specifically relative to the target person rather than to other members.

FAN Club

Let’s take a deeper look at the nature of the FAN Club. We have already mentioned that its clusters are overlapping, or non-exclusive, and citied the example that not all family are neighbours and vice versa. In fact, the clusters are simply the target’s associates grouped for priority of investigation. The FAN QuickSheet contains a diagram illustrating the concept of targeted research, where concentric circles represent clusters of associates, ordered according to the strength of their connection with the target, which might be searched from the innermost outwards.

  • Target person
  • Known relatives and in-laws
  • Others of same surname
  • Associates and neighbours of target
  • Associates of those associates

First, notice that these circles are not literal interpretations of the FAN acronym; it would be a limiting folly to treat the acronym as some simple prescription. Even this diagram is just a guide, though, and we might conceive of more circles according to the nature of the particular problem and some knowledge of the potential sources. For instance, people of the same surname who are also neighbours are more likely to be family members (known or unknown) and so potentially represent a stronger connection than ones elsewhere. I once subdivided neighbours to prioritise ones in the same occupation, based on the assumption that they may have worked in the same place as the target.

So are all these associates just acquaintances? Not really; the use of the word also embraces cases of a general connection, or association, between those persons and the target. In essence, these associations are the properties shared by the members of each cluster. The associates might be acquaintances … or they might be family members who had never met, or persons of the same surname but different family, etc. A favourite of mine, also mentioned in the FAN QuickSheet, is the list of persons interred in the same or neighbouring burial plots because it often throws up surprising further connections.

The targeted-research list, above, illustrates another technique: that of looking at indirect associates, or associates-of-associates in this case. Looking at the FAN Club of an associate may be necessary in order to understand the nature of a connection, or to investigate further connections; this will begin to form a network rather than a set of connections anchored on the target person. The worked example of Mary Smith uses this technique as it looks at the FAN Club of her husband. We can envisage many variations of this, such as family-of-neighbours or family-of-family (including in-laws), but where should we stop? Well, there’s no shortage of scope but an iterative approach, customised for each case, would be more practical than trying to enumerate every possible direct and indirect associate at the outset. The strength of an indirect association depends on the product of the strengths of the individual direct associations, and so it can fall-off very quickly.

A recent case of mine involved the identity of a George Kirk, for whom there was no visible birth/baptism record. By identifying his father (Joseph Kirk), from his second marriage certificate, and so finding his mother (Elizabeth2 Hutchinson), and then looking at her parents (Joshua & Elizabeth1), and then identifying all her siblings, it was possible to show that George was actually Elizabeth’s2 own son, but baptised as the very youngest son of Joshua & Elizabeth1 before she got married. What I’d done was to deliberately look at the family-of-family of George.

Whether we’re looking at direct or indirect associates, looking at related FAN Clubs means that we have intersecting clusters.

Intersection of FAN Clubs for direct and indirect associates
Figure 5 – Intersection of FAN Clubs for direct and indirect connections.

Those intersecting clusters represent the fact that there may be some shared associates. This will be far more likely for a direct associate (i.e. the FAN Club of someone in the target’s FAN Club) than for an indirect associate (e.g. the FAN Club of someone who has an associate in common with the target’s FAN Club).

All this means, of course, is that those lives are interlocked, and the history of one will affect and be affected-by the history of others. Putting it another way: you cannot research an individual in isolation!

It is usually said that whole-family research is a prerequisite for cluster research; however, I will suggest that family reconstitution is a more fundamental notion because it applies to arbitrary families rather than specifically your own. The term is defined in one dictionary as follows: “The technique of compiling family trees for as many people as possible in a chosen area of study, e.g. a parish, so as to obtain detailed demographic data on matters such as age at marriage, or expectation of life”.[7] While this is a fair definition, I take issue with the emphasis on demographics, particularly from a family-history dictionary.

The concept is fundamental, therefore, because it underpins several distinct genealogical pursuits:

  • Whole-family genealogy. While I cannot find a strict definition, it can be described in terms of its differences from direct-line genealogy where only direct ancestors (maternal and paternal) are researched. Whole-family genealogy means that the siblings of every ancestor are also researched, and possibly their descendants too. Either of these may be constrained by surname, such that only direct ancestors with your surname are considered (possibly for establishing a particular pedigree), or only descendants of some single progenitor who carry your surname (usually for a so-called “your family tree”). Whole-family genealogy also means looking at the offspring of any multiple marriages, and also the marriages of the women in each generation.
  • One-name studies. Studying everyone of a given surname, including its variants. This might be worldwide, or it might be constrained by place and/or time period.
  • One-place-studies. Studying the whole population of a given community, such as a village or hamlet.

Family reconstitution is essential for any of these pursuits because it is the first step in establishing the structure of some community; without that then you could not investigate the associations of a family with other families or individuals.

So let’s cast the net even further: what about micro-history? Well, all of the above pursuits are variations of micro-history, but my own use of this term would also include historical subjects other than persons, such as places, groups (e.g. regiments, companies, classes), and animals.

Genealogy is almost always about persons rather than places, or any other historical subject, but the same method would be applicable in all cases. For instance, we could analyse places in a similar manner to persons, and establish the identity of a place reference through an examination of its associations. This would force a difference between treating a place as some property for clustering persons and treating it as an independent entity with its own identity and associations. In reality, establishing the identity of a given person may first require the identity of some place to be established, and so it is artificial to think of these cases as fundamentally separate.

Link Analysis

Having found items of relevant information in the extra sources from the community context, it is then time to make an argument for the identity of some person, or for the biological relationships within some family. We’re now out of cluster analysis and into another long-standing method: Link Analysis.

This Wikipedia page actually gives a pretty poor summary of link analysis; it gives the impression that it is all about large-scale software processing of connections found in bulk data. While this may be the current usage, it is a method that predates the computer age, and it was originally used as a way of visualising connections in logical deduction — see the introduction to Our Days of Future Passed — Part III.

Use of Link Analysis for analysing and correlating source information
Figure 6 – Use of Link Analysis for analysing and correlating source information.

In other words, the application of genealogy software to this method would primarily be about visualisation, and helping us keep track of sources, information, and specific references. An Internet search for images relating to “Link Analysis Software” gives many ideas for visualisation, but they all share the same fault: they are node-heavy and assume that most of the information is related to the objects (nodes) rather than to the links (edges). In a genealogical context, each link would have to embrace any quoted information (or links to associated transcriptions), the relevant source, and our analysis or deduction (in narrative form), whereas the objects would mostly represent person references (as opposed to identified people who lived).

So where would the transition arise between clusters and links? We have already mentioned that cluster research, and the FAN Principle, employ cluster analysis in terms of categorising persons for targeted research. That research would find relevant information that could be used to create an argument for establishing someone’s identity, but it is highly unlikely that any single item will be enough to achieve this. Correlating and comparing those items is where link analysis would be employed, irrespective of whether this was done in your head, with a pencil and paper, or with some new software tool.

Conclusion

My original intention with this analysis was to look at the essence of the FAN Principle, and so understand how it is applied to address specific research problems; to compare this research method with certain ones outside of genealogy; and to understand the relationship between these methods and the various genealogical (or historical) goals that we may aspire to. Out of this deeper understanding of the overall landscape should come the ability to develop software that might better help in those pursuits — and particularly in their visualisations — as opposed to simply interpreting the FAN acronym literally.

What I didn’t anticipate was the level to which cluster research applies in all veins of genealogy. Just as historical context is essential for the study of historical events, so community context is essential in the study of individuals. It is probably one of the most fundamental concepts in any historical research, and it lies at the heart of many of its pursuits, in additional to its application to solving difficult identity problems. What a shame, then, that modern Internet genealogy encourages people to deal only with direct answers to simple questions; more Charlie foxtrot than cluster research!

Inferential genealogy should be a concept applicable to everyone’s research, but it has sadly become associated with the professional or the academic. I have already heaped much of the blame for this on commercial genealogy’s simplistic model (see Reaping What We Sow — Part I and Reaping What We Sow — Part II) but can anything be done to counter it? Some inferential cases may be very complicated and involve extensive associations in order to make an argument, but not all will be so complicated as to be out of the reach of the more ordinary genealogist. What is missing is a set of powerful tools for visualising the associations, and supporting our inferences by accepting written narrative at appropriate places. Of course, it would have to be a benefit rather than a chore, and the easier it was to use then the greater that benefit.





[1] Elizabeth Shown Mills, QuickSheet: The Historical Biographer's Guide to Cluster Research (The FAN Principle) (Baltimore: Genealogical Publishing Co., 2012); hereinafter cited as FAN QuickSheet.
[2] Elizabeth Shown Mills, “QuickLesson 11: Identity Problems & the FAN Principle”, Evidence Explained: Historical Analysis, Citation & Source Usage (https://www.evidenceexplained.com/content/quicklesson-11-identity-problems-fan-principle : posted 26 Aug 2012, accessed 24 Oct 2016); hereinafter cited as QuickLesson 11.
[3] From Mark Anthony’s speech in William Shakespeare’s play: Julius Caesar.
[4] In genealogy, that is. The term “Fan principle” may also be a reference to the “Fan dipole” antenna design for optimised bandwidth, or the “Fan-Line Principle” that employs three fan lines in stock market predictions.
[5] Mills, FAN QuickSheet.
[6] “Cluster genealogy”, Wikipedia (https://en.wikipedia.org/wiki/Cluster_genealogy : accessed 4 Nov 2016).
[7] David Hey, The Oxford Dictionary of Local and Family History (Oxford University Press, 1997),s.v. “family reconstitution”.